Quantitative imaging of thickness maps using ultrasonic guided wave tomography based on full-waveform inversion

2016 ◽  
Author(s):  
Rao Jing ◽  
Ratassepp Madis ◽  
Fan Zheng
Keyword(s):  
Quantitative Imaging ◽  
Waveform Inversion ◽  
Guided Wave ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Ultrasonic Guided Wave ◽  
Wave Tomography

Ultrasonic computed tomography based on full-waveform inversion for bone quantitative imaging

2017 ◽  
Vol 62 (17) ◽  
pp. 7011-7035 ◽  
Author(s):  
Simon Bernard ◽  
Vadim Monteiller ◽  
Dimitri Komatitsch ◽  
Philippe Lasaygues
Keyword(s):  
Computed Tomography ◽  
Quantitative Imaging ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform

scholarly journals Quantitative mapping of thickness variations along a ray path using geometrical full waveform inversion and guided wave mode conversion

2022 ◽  
Vol 478 (2257) ◽  
Author(s):  
Peng Zuo ◽  
Peter Huthwaite
Keyword(s):  
Guided Waves ◽  
Mode Conversion ◽  
Waveform Inversion ◽  
Guided Wave ◽  
Direct Access ◽  
Thickness Variation ◽  
Full Waveform Inversion ◽  
Large Area ◽  
Wave Inversion ◽  
Full Waveform

Quantitative guided wave thickness mapping in plate-like structures and pipelines is of significant importance for the petrochemical industry to accurately estimate the minimum remaining wall thickness in the presence of corrosion, as guided waves can inspect a large area without needing direct access. Although a number of inverse algorithms have been studied and implemented in guided wave reconstruction, a primary assumption is widely used: the three-dimensional guided wave inversion of thickness is simplified as a two-dimensional acoustic wave inversion of velocity, with the dispersive nature of the waves linking thickness to velocity. This assumption considerably simplifies the inversion procedure; however, it makes it impossible to account for mode conversion. In reality, mode conversion is quite common in guided wave scattering with asymmetric wall loss, and compared with non-converted guided wave modes, converted modes may provide greater access to valuable information about the thickness variation, which, if exploited, could lead to improved performance. Geometrical full waveform inversion (GFWI) is an ideal tool for this, since it can account for mode conversion. In this paper, quantitative thickness reconstruction based on GFWI is developed in a plate cross-section and applied to study the performance of thickness reconstruction using mode conversion.

scholarly journals Quantitative Inspection of Complex-Shaped Parts Based on Ice-Coupled Ultrasonic Full Waveform Inversion Technology

2021 ◽  
Vol 11 (10) ◽  
pp. 4433
Author(s):  
Wenjin Xu ◽  
Maodan Yuan ◽  
Weiming Xuan ◽  
Xuanrong Ji ◽  
Yan Chen
Keyword(s):  
Turbine Blade ◽  
Signal To Noise Ratio ◽  
Quantitative Imaging ◽  
Waveform Inversion ◽  
Pseudospectral Method ◽  
Quantitative Information ◽  
Imaging Method ◽  
Full Waveform Inversion ◽  
Velocity Models ◽  
Full Waveform

Ultrasonic methods have been extensively developed in nondestructive testing for various materials and components. However, accurately extracting quantitative information about defects still remains challenging, especially for complex structures. Although the immersion technique is commonly used for complex-shaped parts, the large mismatch of acoustic impedance between water and metal prevents effective ultrasonic transmission and leads to a low signal-to-noise ratio(SNR). In this paper, a quantitative imaging method is proposed for complex-shaped parts based on an ice-coupled full waveform inversion (FWI) method. Numerical experiments were carried out to quantitatively inspect the various defects in a turbine blade. Firstly, the k-space pseudospectral method was applied to simulate ice-coupled ultrasonic testing for the turbine blade. The recorded full wavefields were then applied for a frequency-domain FWI based on the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method. With a carefully selected iterative number and frequency, a successive-frequency FWI can well detect half wavelength defects. Extended studies on an open notch with different orientations and multiple adjacent defects proved its capability to detect different types of defects. Finally, an uncertainty analysis was conducted with inaccurate initial velocity models with a relative error of ± 2%, demonstrating its robustness even with a certain inaccuracy. This study demonstrates that the proposed method has a high potential to inspect complex-shaped structures with an excellent resolution.

Robust Guided Wave Tomography Method for Large and Irregular Defects

2021 ◽  
Author(s):  
Junkai Tong ◽  
Min Lin ◽  
Xiaocen Wang ◽  
Jiahao Ren ◽  
Jian Li ◽  
...  
Keyword(s):  
Complex Shape ◽  
Waveform Inversion ◽  
Guided Wave ◽  
Diffraction Tomography ◽  
Multiple Defects ◽  
Full Waveform ◽  
Speed Advantage ◽  
Wave Tomography ◽  
Tomography Method ◽  
Defect Imaging

Abstract Finding a fast, robust way to quantitatively measuring the remaining wall thickness of complex structures when multiple defects exist is one of the leading challenges in Nondestructive Testing (NDT). Traditional inversion algorithms like ray tomography and full waveform inversion (FWI) suffered from problems like convergence, limited resolution and slow speed. Diffraction tomography (DT) has speed advantage over the preceding methods and its resolution can be further amplified by integrating with other methods like bent-ray tomography and iteration. However, DT can only detect shallow and small defects. Compared with those methods, convolutional neural network (CNN) opens a new way for quantitative defect imaging, as with pre-trained data it can achieve significant speed and resolution than the traditional methods. In this paper, we investigated the performance of CNN in imaging multiple defects and the inversion results show that when dealing with multiple defects with complex shape on a plate-like structure, CNN can achieve better resolution than other methods with maximum errors below 0.54mm in most regions. This research provides the experimental guidance for future study in finding the possible ways to improve the resolution of the algorithms.

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